import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
	from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_UpperCAmelCase          =						logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase          =						256





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   ['''melgan''']
				def __init__(		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      ):



						"""simple docstring"""





						super().__init__()

						# From MELGAN
						A_						:							Union[str, Any]      = math.log(1E-5   )  # Matches MelGAN training.
						A_						:							Union[str, Any]      = 4.0  # Largest value for most examples
						A_						:							List[str]      = 1_2_8

						self.register_modules(
						    notes_encoder=lowercase						,      continuous_encoder=lowercase						,      decoder=lowercase						,      scheduler=lowercase						,      melgan=lowercase						,      )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=(-1.0, 1.0)						,      lowercase=False   ):



						"""simple docstring"""





						A_  ,     A_						:							Any      = output_range
						if clip:
								A_						:							Tuple      = torch.clip(lowercase						,      self.min_value						,      self.max_value   )
						# Scale to [0, 1].
						A_						:							Optional[int]      = (features - self.min_value) / (self.max_value - self.min_value)
						# Scale to [min_out, max_out].
						return zero_one * (max_out - min_out) + min_out
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=(-1.0, 1.0)						,      lowercase=False   ):



						"""simple docstring"""





						A_  ,     A_						:							Dict      = input_range
						A_						:							Any      = torch.clip(lowercase						,      lowercase						,      lowercase   ) if clip else outputs
						# Scale to [0, 1].
						A_						:							str      = (outputs - min_out) / (max_out - min_out)
						# Scale to [self.min_value, self.max_value].
						return zero_one * (self.max_value - self.min_value) + self.min_value
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase   ):



						"""simple docstring"""





						A_						:							Optional[int]      = input_tokens > 0
						A_  ,     A_						:							Tuple      = self.notes_encoder(
						    encoder_input_tokens=lowercase						,      encoder_inputs_mask=lowercase   )

						A_  ,     A_						:							int      = self.continuous_encoder(
						    encoder_inputs=lowercase						,      encoder_inputs_mask=lowercase   )

						return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase   ):



						"""simple docstring"""





						A_						:							Optional[int]      = noise_time
						if not torch.is_tensor(lowercase   ):
								A_						:							List[str]      = torch.tensor([timesteps]						,      dtype=torch.long						,      device=input_tokens.device   )
						elif torch.is_tensor(lowercase   ) and len(timesteps.shape   ) == 0:
								A_						:							str      = timesteps[None].to(input_tokens.device   )

						# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
						A_						:							str      = timesteps * torch.ones(input_tokens.shape[0]						,      dtype=timesteps.dtype						,      device=timesteps.device   )

						A_						:							List[Any]      = self.decoder(
						    encodings_and_masks=lowercase						,      decoder_input_tokens=lowercase						,      decoder_noise_time=lowercase   )
						return logits







				@torch.no_grad()
				def __call__(		self						,      lowercase						,      lowercase = None						,      lowercase = 1_0_0						,      lowercase = True						,      lowercase = "numpy"						,      lowercase = None						,      lowercase = 1						,      ):



						"""simple docstring"""





						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(lowercase						,      lowercase   ) or callback_steps <= 0)
						):
								raise ValueError(
								    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
								    F''' {type(lowercase   )}.'''   )

						A_						:							int      = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]						,      dtype=np.floataa   )
						A_						:							Any      = np.zeros([1, 0, self.n_dims]						,      np.floataa   )
						A_						:							List[Any]      = torch.ones((1, TARGET_FEATURE_LENGTH)						,      dtype=lowercase						,      device=self.device   )

						for i, encoder_input_tokens in enumerate(lowercase   ):
								if i == 0:
										A_						:							Optional[Any]      = torch.from_numpy(pred_mel[:1].copy()   ).to(
										    device=self.device						,      dtype=self.decoder.dtype   )
										# The first chunk has no previous context.
										A_						:							str      = torch.zeros((1, TARGET_FEATURE_LENGTH)						,      dtype=lowercase						,      device=self.device   )
								else:
										# The full song pipeline does not feed in a context feature, so the mask
										# will be all 0s after the feature converter. Because we know we're
										# feeding in a full context chunk from the previous prediction, set it
										# to all 1s.
										A_						:							List[Any]      = ones

								A_						:							List[str]      = self.scale_features(
								    lowercase						,      output_range=[-1.0, 1.0]						,      clip=lowercase   )

								A_						:							Union[str, Any]      = self.encode(
								    input_tokens=torch.IntTensor([encoder_input_tokens]   ).to(device=self.device   )						,      continuous_inputs=lowercase						,      continuous_mask=lowercase						,      )

								# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
								A_						:							Optional[Any]      = randn_tensor(
								    shape=encoder_continuous_inputs.shape						,      generator=lowercase						,      device=self.device						,      dtype=self.decoder.dtype						,      )

								# set step values
								self.scheduler.set_timesteps(lowercase   )

								# Denoising diffusion loop
								for j, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										A_						:							Optional[Any]      = self.decode(
										    encodings_and_masks=lowercase						,      input_tokens=lowercase						,      noise_time=t / self.scheduler.config.num_train_timesteps						,      )

										# Compute previous output: x_t -> x_t-1
										A_						:							Optional[Any]      = self.scheduler.step(lowercase						,      lowercase						,      lowercase						,      generator=lowercase   ).prev_sample

								A_						:							int      = self.scale_to_features(lowercase						,      input_range=[-1.0, 1.0]   )
								A_						:							List[Any]      = mel[:1]
								A_						:							Tuple      = mel.cpu().float().numpy()

								A_						:							Dict      = np.concatenate([full_pred_mel, pred_mel[:1]]						,      axis=1   )

								# call the callback, if provided
								if callback is not None and i % callback_steps == 0:
										callback(lowercase						,      lowercase   )

								logger.info('Generated segment'						,      lowercase   )

						if output_type == "numpy" and not is_onnx_available():
								raise ValueError(
								    'Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'   )
						elif output_type == "numpy" and self.melgan is None:
								raise ValueError(
								    'Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'   )

						if output_type == "numpy":
								A_						:							int      = self.melgan(input_features=full_pred_mel.astype(np.floataa   )   )
						else:
								A_						:							Tuple      = full_pred_mel

						if not return_dict:
								return (output,)

						return AudioPipelineOutput(audios=lowercase   )







import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_UpperCAmelCase          =						collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_UpperCAmelCase          =						"""https://storage.googleapis.com/cvdf-datasets/mnist/"""




def 							UpperCamelCase		(			__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							Tuple      = numpy.dtype(numpy.uintaa      ).newbyteorder('>'      )
		return numpy.frombuffer(bytestream.read(4      )		,dtype=__lowercase      )[0]




@deprecated(__lowercase		,'Please use tf.data to implement this functionality.'      )
def 							UpperCamelCase		(			__lowercase		:					List[str]      ):
		'''simple docstring'''




		print('Extracting'		,f.name      )
		with gzip.GzipFile(fileobj=__lowercase      ) as bytestream:
				A_						:							str      = _readaa(__lowercase      )
				if magic != 20_51:
						raise ValueError(
						    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)      )
				A_						:							Optional[int]      = _readaa(__lowercase      )
				A_						:							List[Any]      = _readaa(__lowercase      )
				A_						:							Any      = _readaa(__lowercase      )
				A_						:							List[str]      = bytestream.read(rows * cols * num_images      )
				A_						:							Optional[int]      = numpy.frombuffer(__lowercase		,dtype=numpy.uinta      )
				A_						:							Union[str, Any]      = data.reshape(__lowercase		,__lowercase		,__lowercase		,1      )
				return data




@deprecated(__lowercase		,'Please use tf.one_hot on tensors.'      )
def 							UpperCamelCase		(			__lowercase		:					Optional[int]		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							Dict      = labels_dense.shape[0]
		A_						:							List[str]      = numpy.arange(__lowercase      ) * num_classes
		A_						:							Any      = numpy.zeros((num_labels, num_classes)      )
		A_						:							List[Any]      = 1
		return labels_one_hot




@deprecated(__lowercase		,'Please use tf.data to implement this functionality.'      )
def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					Tuple=False		,__lowercase		:					List[str]=10      ):
		'''simple docstring'''




		print('Extracting'		,f.name      )
		with gzip.GzipFile(fileobj=__lowercase      ) as bytestream:
				A_						:							int      = _readaa(__lowercase      )
				if magic != 20_49:
						raise ValueError(
						    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)      )
				A_						:							List[Any]      = _readaa(__lowercase      )
				A_						:							Dict      = bytestream.read(__lowercase      )
				A_						:							List[str]      = numpy.frombuffer(__lowercase		,dtype=numpy.uinta      )
				if one_hot:
						return _dense_to_one_hot(__lowercase		,__lowercase      )
				return labels





class UpperCAmelCase		:


				'''simple docstring'''






				@deprecated(
				    lowercase						,      'Please use alternatives such as official/mnist/_DataSet.py'
				    ' from tensorflow/models.'						,      )
				def __init__(		self						,      lowercase						,      lowercase						,      lowercase=False						,      lowercase=False						,      lowercase=dtypes.floataa						,      lowercase=True						,      lowercase=None						,      ):



						"""simple docstring"""





						A_  ,     A_						:							Dict      = random_seed.get_seed(lowercase   )
						# If op level seed is not set, use whatever graph level seed is returned
						numpy.random.seed(seeda if seed is None else seeda   )
						A_						:							Optional[int]      = dtypes.as_dtype(lowercase   ).base_dtype
						if dtype not in (dtypes.uinta, dtypes.floataa):
								raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype   )
						if fake_data:
								A_						:							Optional[int]      = 1_0_0_0_0
								A_						:							int      = one_hot
						else:
								assert (
								    images.shape[0] == labels.shape[0]
								), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
								A_						:							Dict      = images.shape[0]

								# Convert shape from [num examples, rows, columns, depth]
								# to [num examples, rows*columns] (assuming depth == 1)
								if reshape:
										assert images.shape[3] == 1
										A_						:							int      = images.reshape(
										    images.shape[0]						,      images.shape[1] * images.shape[2]   )
								if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										A_						:							Dict      = images.astype(numpy.floataa   )
										A_						:							Optional[int]      = numpy.multiply(lowercase						,      1.0 / 255.0   )
						A_						:							Optional[int]      = images
						A_						:							List[Any]      = labels
						A_						:							str      = 0
						A_						:							Union[str, Any]      = 0
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._images
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._labels
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._num_examples
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._epochs_completed







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=False						,      lowercase=True   ):



						"""simple docstring"""





						if fake_data:
								A_						:							List[Any]      = [1] * 7_8_4
								A_						:							Dict      = [1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(lowercase   )],
								    [fake_label for _ in range(lowercase   )],
								)
						A_						:							int      = self._index_in_epoch
						# Shuffle for the first epoch
						if self._epochs_completed == 0 and start == 0 and shuffle:
								A_						:							Tuple      = numpy.arange(self._num_examples   )
								numpy.random.shuffle(lowercase   )
								A_						:							Dict      = self.images[perma]
								A_						:							Optional[Any]      = self.labels[perma]
						# Go to the next epoch
						if start + batch_size > self._num_examples:
								# Finished epoch
								self._epochs_completed += 1
								# Get the rest examples in this epoch
								A_						:							List[Any]      = self._num_examples - start
								A_						:							Optional[int]      = self._images[start : self._num_examples]
								A_						:							Optional[int]      = self._labels[start : self._num_examples]
								# Shuffle the data
								if shuffle:
										A_						:							Tuple      = numpy.arange(self._num_examples   )
										numpy.random.shuffle(lowercase   )
										A_						:							Dict      = self.images[perm]
										A_						:							Any      = self.labels[perm]
								# Start next epoch
								A_						:							Any      = 0
								A_						:							Optional[Any]      = batch_size - rest_num_examples
								A_						:							Optional[int]      = self._index_in_epoch
								A_						:							Dict      = self._images[start:end]
								A_						:							Any      = self._labels[start:end]
								return (
								    numpy.concatenate((images_rest_part, images_new_part)						,      axis=0   ),
								    numpy.concatenate((labels_rest_part, labels_new_part)						,      axis=0   ),
								)
						else:
								self._index_in_epoch += batch_size
								A_						:							Tuple      = self._index_in_epoch
								return self._images[start:end], self._labels[start:end]




@deprecated(__lowercase		,'Please write your own downloading logic.'      )
def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					Optional[int]		,__lowercase		:					Any      ):
		'''simple docstring'''




		if not gfile.Exists(__lowercase      ):
				gfile.MakeDirs(__lowercase      )
		A_						:							Optional[Any]      = os.path.join(__lowercase		,__lowercase      )
		if not gfile.Exists(__lowercase      ):
				urllib.request.urlretrieve(__lowercase		,__lowercase      )  # noqa: S310
				with gfile.GFile(__lowercase      ) as f:
						A_						:							Optional[int]      = f.size()
				print('Successfully downloaded'		,__lowercase		,__lowercase		,'bytes.'      )
		return filepath




@deprecated(
    __lowercase		,'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'      )
def 							UpperCamelCase		(			__lowercase		:					Dict		,__lowercase		:					str=False		,__lowercase		:					int=False		,__lowercase		:					str=dtypes.floataa		,__lowercase		:					int=True		,__lowercase		:					List[Any]=50_00		,__lowercase		:					Union[str, Any]=None		,__lowercase		:					Union[str, Any]=DEFAULT_SOURCE_URL		,):
		'''simple docstring'''




		if fake_data:

				def fake():
						return _DataSet(
						    []		,[]		,fake_data=__lowercase		,one_hot=__lowercase		,dtype=__lowercase		,seed=__lowercase      )

				A_						:							Union[str, Any]      = fake()
				A_						:							Any      = fake()
				A_						:							str      = fake()
				return _Datasets(train=__lowercase		,validation=__lowercase		,test=__lowercase      )

		if not source_url:  # empty string check
				A_						:							Tuple      = DEFAULT_SOURCE_URL

		A_						:							List[Any]      = 'train-images-idx3-ubyte.gz'
		A_						:							Optional[int]      = 'train-labels-idx1-ubyte.gz'
		A_						:							Optional[Any]      = 't10k-images-idx3-ubyte.gz'
		A_						:							Optional[int]      = 't10k-labels-idx1-ubyte.gz'

		A_						:							str      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + train_images_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							Optional[int]      = _extract_images(__lowercase      )

		A_						:							Optional[Any]      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + train_labels_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							Union[str, Any]      = _extract_labels(__lowercase		,one_hot=__lowercase      )

		A_						:							Any      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + test_images_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							str      = _extract_images(__lowercase      )

		A_						:							Union[str, Any]      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + test_labels_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							int      = _extract_labels(__lowercase		,one_hot=__lowercase      )

		if not 0 <= validation_size <= len(__lowercase      ):
				A_						:							Dict      = (
				    'Validation size should be between 0 and '
				    f'''{len(__lowercase      )}. Received: {validation_size}.'''
				)
				raise ValueError(__lowercase      )

		A_						:							int      = train_images[:validation_size]
		A_						:							List[str]      = train_labels[:validation_size]
		A_						:							Tuple      = train_images[validation_size:]
		A_						:							Dict      = train_labels[validation_size:]

		A_						:							str      = {'dtype': dtype, 'reshape': reshape, 'seed': seed}

		A_						:							Optional[int]      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )
		A_						:							int      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )
		A_						:							int      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )

		return _Datasets(train=__lowercase		,validation=__lowercase		,test=__lowercase      )






